Compressed-Rule-Ensembles

R Implementation of Compressed Rule Ensemble Models introduced in "Compressed Rule Ensemble Learning) (2022) by Malte Nalenz and Thomas Augustin, accepted for publication at the AIStats 2022 conference.

Install

library(devtools)
devtools::install_git("https://github.com/maltenlz/Compressed-Rule-Ensembles")
library(cre)

Example on simulated data

Draw data from mixture of normals:

x1 = c(rnorm(100,-1, 1), rnorm(100, 1, 1))
x2 = c(rnorm(100,-1, 1), rnorm(100, 1, 1))
x = cbind(x1, x2)
y = c(rep(1, times = 100), rep(0, times = 100))

Run the CRE model with default settings:

cre_mod = cre(x, y, task = "class")

Predict in-sample:

predict(cre_mod, x)

Look at the most important rules:

important_rules(cre_mod)

Also look the distribution of split points:

visualise_clusters(cre_mod)

This package is actively developed with more extensions coming soon...